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Unread book in perfect condition. N° de ref. del artículo 46831944
Acerca del autor:
Gilson Antonio Giraldi is a Researcher at the National Laboratory for Scientific Computing (LNCC), Brazil, where he is responsible for academic research projects in image analysis, statistical and machine learning, scientific visualization, and physically-based animation. He holds a PhD in Computer Graphics (2000) from the Federal University of Rio de Janeiro, Brazil, and has a degree in Mathematics (1986) from the Pontifical Catholic University of Campinas, Brazil.
Antonio Lopes Apolinário Junior is an Associate Professor at the Federal University of Bahia (UFBA), Brazil. He holds a PhD in Systems and Computer Engineering (2004) from the Federal University of Rio de Janeiro, Brazil. His research interests lie in computer graphics, 3D modeling, augmented reality, virtual reality, and physically-based rendering and animation.
Leandro Tavares da Silva is a Professor at the Federal Center for Technological Education “Celso Suckow da Fonseca” (CEFET-RJ), Brazil. He holds a PhD in Computational Modeling (2016) from the National Laboratory for Scientific Computing (LNCC), Brazil. He currently does research on fluid simulation and animation, and deep learning.
Liliane Rodrigues de Almeida is a Fellow Researcher at the National Laboratory for Scientific Computing (LNCC). She holds a Master’s degree in Computer Science (2017) from the Federal University of Juiz de Fora (UFJF), Brazil, and has a degree in Computer Science from the same university. Her fields of research are physical simulation and computational geometry.
Título: Deep Learning for Fluid Simulation and ...
Editorial: Springer
Año de publicación: 2023
Encuadernación: Encuadernación de tapa blanda
Condición: As New
Librería: Buchpark, Trebbin, Alemania
Condición: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | This book is an introduction to the use of machine learning and data-driven approaches in fluid simulation and animation, as an alternative to traditional modeling techniques based on partial differential equations and numerical methods ¿ and at a lower computational cost.This work starts with a brief review of computability theory, aimed to convince the reader ¿ more specifically, researchers of more traditional areas of mathematical modeling ¿ about the power of neural computing in fluid animations. In these initial chapters, fluid modeling through Navier-Stokes equations and numerical methods are also discussed.The following chapters explore the advantages of the neural networks approach and show the building blocks of neural networks for fluid simulation. They cover aspects related to training data, data augmentation, and testing. The volume completes with two case studies, one involving Lagrangian simulation of fluids using convolutional neural networks and the other using Generative Adversarial Networks (GANs) approaches. Nº de ref. del artículo: 42808253/1
Cantidad disponible: 2 disponibles
Librería: Majestic Books, Hounslow, Reino Unido
Condición: New. pp. 176. Nº de ref. del artículo: 397855440
Cantidad disponible: 4 disponibles
Librería: moluna, Greven, Alemania
Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Discloses the use of machine learning in fluid simulation as an option of lower computational costOffers a comparison between two neural network approaches and corresponding modelsIntended for students and researchers who need to keep pace . Nº de ref. del artículo: 945398630
Cantidad disponible: Más de 20 disponibles
Librería: Books Puddle, New York, NY, Estados Unidos de America
Condición: New. pp. 176. Nº de ref. del artículo: 26398554383
Cantidad disponible: 4 disponibles
Librería: Biblios, Frankfurt am main, HESSE, Alemania
Condición: New. pp. 176. Nº de ref. del artículo: 18398554373
Cantidad disponible: 4 disponibles
Librería: preigu, Osnabrück, Alemania
Taschenbuch. Condición: Neu. Deep Learning for Fluid Simulation and Animation | Fundamentals, Modeling, and Case Studies | Gilson Antonio Giraldi (u. a.) | Taschenbuch | xii | Englisch | 2023 | Springer | EAN 9783031423321 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. Nº de ref. del artículo: 127335785
Cantidad disponible: 5 disponibles
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book is an introduction to the use of machine learning and data-driven approaches in fluid simulation and animation, as an alternative to traditional modeling techniques based on partial differential equations and numerical methods - and at a lower computational cost.This work starts with a brief review of computability theory, aimed to convince the reader - more specifically, researchers of more traditional areas of mathematical modeling - about the power of neural computing in fluid animations. In these initial chapters, fluid modeling through Navier-Stokes equations and numerical methods are also discussed.The following chapters explore the advantages of the neural networks approach and show the building blocks of neural networks for fluid simulation. They cover aspects related to training data, data augmentation, and testing.The volume completes with two case studies, one involving Lagrangian simulation of fluids using convolutional neural networks and the other using Generative Adversarial Networks (GANs) approaches. 164 pp. Englisch. Nº de ref. del artículo: 9783031423321
Cantidad disponible: 2 disponibles
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book is an introduction to the use of machine learning and data-driven approaches in fluid simulation and animation, as an alternative to traditional modeling techniques based on partial differential equations and numerical methods - and at a lower computational cost.This work starts with a brief review of computability theory, aimed to convince the reader - more specifically, researchers of more traditional areas of mathematical modeling - about the power of neural computing in fluid animations. In these initial chapters, fluid modeling through Navier-Stokes equations and numerical methods are also discussed.The following chapters explore the advantages of the neural networks approach and show the building blocks of neural networks for fluid simulation. They cover aspects related to training data, data augmentation, and testing.The volume completes with two case studies, one involving Lagrangian simulation of fluids using convolutional neural networks and the other using Generative Adversarial Networks (GANs) approaches. Nº de ref. del artículo: 9783031423321
Cantidad disponible: 1 disponibles
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Taschenbuch. Condición: Neu. Neuware -This book is an introduction to the use of machine learning and data-driven approaches in fluid simulation and animation, as an alternative to traditional modeling techniques based on partial differential equations and numerical methods ¿ and at a lower computational cost.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 180 pp. Englisch. Nº de ref. del artículo: 9783031423321
Cantidad disponible: 2 disponibles
Librería: Basi6 International, Irving, TX, Estados Unidos de America
Condición: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service. Nº de ref. del artículo: ABEOCT25-271552
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